Personal Robotics: Deep Learning for Grasping Novel Objects
Robot Learning Lab Personal Robotics, Co-Robots, Robotic Perception. Computer Science Department, Cornell University. Learning-based approaches in previous works have been succeesfully used for grasping novel objects, but required manual design of features for image and depth data. We use deep learning, which allow us to learn the basic features used by our algorithm directly from RGB-D data. Our
パーティクルフィルタ - おべんきょうwiki
時系列フィルタの一種． 最適解を求めるためにモンテカルロ法を用いるのが特徴． 分野によっては別の名前で呼ばれたりする． 理論については下の北川先生の解説が詳しい．
KLT: Kanade-Lucas-Tomasi Feature Tracker
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker KLT is an implementation, in the C programming language, of a feature tracker for the computer vision community. The source code is in the public domain, available for both commercial and non-commerical use. The tracker is based on the early work of Lucas and Kanade [1], was developed fully by Tomasi and Kanade [2], and was explain
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Parthipan Siva( Research - Simple Tracker )
Code - Sam Hare
